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Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics

NNFMM: MGMT & MINIMSTN UNCRTNTS ERRORS NMRCL AERODYNMCS

Engineering

Mathematics & Science

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B2B GT

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B2B & B2C

This volume reports results from the German research initiative MUNA (Management and Minimization of Errors and Uncertainties in Numerical Aerodynamics), which combined development activities of the German Aerospace Center (DLR), German universities and German aircraft industry. The main objective of this five year project was the development of methods and procedures aiming at reducing various types of uncertainties that are typical of numerical flow simulations. The activities were focused on methods for grid manipulation, techniques for increasing the simulation accuracy, sensors for turbulence modelling, methods for handling uncertainties of the geometry and grid deformation as well as stochastic methods for quantifying aleatoric uncertainties.

COLFR

POST SECONDARY FOUR YEAR

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COLJR

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Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Preface.
Contents.
1: Mesh Generation and Manipulation.
2: Methods and Strategies for the Detection and Management of Grid Induced Uncertainties in Numerical Aerodynamics.
3: Quantification and Reduction of Numerical Uncertainties by Improvement of the TAU Grid Adaptation Tool and Adjoint Methods.
4: Application of Mesh Modifications and Adjoint Error Estimates.
5: Turbulence Modeling.
6: Minimization and Quantification of Errors and Uncertainties in RANS Modeling.
7: Sensor Controlled Zonal Rans-LES Method.
8: Numerical Methods.
9: The Application of Iterated Defect Corrections Based on Weno Reconstruction.
10: Geometry and Deformation.
11: Uncertainties of Numerical Structural Models in the Frame of Aeroelasticity.
12: A Comparison of Fluid/Structure Coupling Methods for Reduced Structural Models.
13: Improved Mesh Deformation.
14: Stochastic Uncertainties.
15: Statistical Analysis of Parameter Variations Using the Taguchi Method.
16: Numerical Methods for Uncertainty Quantification and Bayesian Update in Aerodynamics.
17: Efficient Quantification of Aerodynamic Uncertainties Using Gradient-Employing Surrogate Methods.
18: Optimal Aerodynamic Design Under Uncertainty.
Author Index.

Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics.css-1p8rvzy{position:absolute;padding-left:8px;}

Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics